Phenology Metrics

Extract phenological metrics from NDVI/EVI time series data. Computes SOS, EOS, LOS, peak value/date, amplitude, and integral using threshold, derivative, or double logistic fitting methods.

Extract vegetation phenology metrics from NDVI/EVI time series. Supports threshold, derivative, and double logistic fitting methods.

Features

• Key metrics: SOS, EOS, LOS, Peak value, Peak date, Amplitude, Integral
• Threshold method: 10%/50% of amplitude ratio
• Derivative method: Inflection points of fitted curve
• Logistic fitting: Double logistic function for smooth phenology
• Input formats: CSV (date, value) or multi-band GeoTIFF stack
• Output formats: CSV and JSON

Phenology Metrics

Metric	Description
SOS	Start of Season (green-up)
EOS	End of Season (senescence)
LOS	Length of Season (EOS - SOS)
Peak	Maximum NDVI/EVI value
Peak Date	Date of peak value
Amplitude	Peak - baseline
Integral	Area under curve (season productivity)

Usage

Extract from CSV time series

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_series.csv \
  --date-col date --value-col ndvi \
  --method threshold --threshold-ratio 0.5 \
  --output phenology.json

Fit double logistic curve

bash

python scripts\phenology-metrics.py fit \
  --input ndvi_series.csv \
  --date-col date --value-col ndvi \
  --output fitted.json --plot-data plot.csv

Generate plot data

bash

python scripts\phenology-metrics.py plot-data \
  --input ndvi_series.csv --date-col date --value-col ndvi \
  --output curve.csv

Installation

bash

pip install numpy>=1.21.0 scipy>=1.7.0 pandas>=1.3.0
# Or: pip install -r scripts/requirements.txt

Parameters

• --input: Input CSV file (date, value columns)
• --date-col: Date column name (default: date)
• --value-col: Value column name (default: ndvi)
• --method: Extraction method (threshold, derivative, logistic)
• --threshold-ratio: Threshold ratio for SOS/EOS (default: 0.5)
• --output: Output file path (CSV or JSON)
• --plot-data: Output fitted curve data for plotting
• --json: Output as JSON

Output

• Phenology metrics: CSV/JSON with all computed metrics
• Fitted curve: CSV with date, original, fitted values
• Statistics: Goodness of fit (R²)

Dependencies

text

numpy>=1.21.0
scipy>=1.7.0
pandas>=1.3.0

GeoTIFF Stack Usage

Use a multi-band GeoTIFF stack (one band per time step) as input:

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_stack.tif \
  --input-format geotiff \
  --date-file dates.txt \
  --method logistic \
  --output phenology.json

• --input-format geotiff: Specifies GeoTIFF stack input
• --date-file: Text file with one date per line (YYYY-MM-DD), one per band
• --band-epoch: Reference date for band numbering (default: band 1 = first date)

Date Format Specification

Supported date formats:

Format	Example	Notes
ISO date	2023-06-15	Recommended (YYYY-MM-DD)
US date	06/15/2023	MM/DD/YYYY
DOY	166	Day of year (1-366)
Year-DOY	2023-166	Combined format

Specify with --date-format parameter.

Method Selection Guidance

Method	Best For	Pros / Cons
threshold	Quick estimation, sparse data	Simple but less precise
derivative	Precise timing, smooth data	Sensitive to noise
logistic	Smooth curves, research-grade	Best fit but needs clean data

Recommendation: Use threshold for exploratory analysis; logistic for publication-quality results.

Missing Data Handling

Time series gaps are common (clouds, sensor issues). The tool handles them via:

• Interpolation: Linear interpolation for gaps ≤30 days (configurable via --max-gap)
• Gap filling: Savitzky-Golay smoothing for noisy data
• Flagging: Outputs data quality flag in results

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_series.csv --method logistic \
  --max-gap 30 --sg-window 7 \
  --output phenology.json

Multi-Year Data Handling

For multi-year time series:

• Per-year extraction: Use --year-range 2020-2023 to loop over each year
• Specify year: Use --year 2022 for single-year analysis
• Phenology metrics: Computed separately per year, output as multi-row CSV/JSON

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_series.csv --method logistic \
  --year-range 2020-2023 \
  --output phenology_multiyear.csv

Batch Processing for Multiple Pixels

Process multiple pixels from a GeoTIFF stack:

bash

python scripts\phenology-metrics.py batch \
  --input ndvi_stack.tif \
  --input-format geotiff \
  --method logistic \
  --output-dir ./phenology_results/ \
  --max-workers 4

Outputs one JSON per pixel (row, col) in the output directory.

Visualization

• Time series: Plot NDVI/EVI points with fitted curve overlay
• Key dates: Vertical lines for SOS, EOS, Peak Date
• Multi-year: Facet plot showing each year's phenology curve

python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('fitted.csv')
plt.plot(df['date'], df['original'], 'o', label='Original')
plt.plot(df['date'], df['fitted'], '-', label='Fitted')
plt.axvline(x=sos_date, color='green', label='SOS')
plt.axvline(x=eos_date, color='red', label='EOS')
plt.legend()
plt.show()

Citation

bibtex

@software{phenology_metrics,
  author  = {ruiduobao},
  title   = {Phenology Metrics Extraction Tool},
  url     = {https://github.com/ruiduobao/phenology-metrics},
  version = {0.1.0},
  year    = {2024},
}

Troubleshooting

Error	Cause	Solution
ConnectionError	Network issue	Check internet, retry
HTTP 429	Rate limit	Wait 60s, retry
ValueError	Invalid input	Check parameter format
Empty output	No valid data	Check input data quality
ModuleNotFoundError	Missing dep	Run pip install
Poor fit (low R²)	Noisy data	Try logistic or increase --sg-window
Missing SOS/EOS	Weak seasonality	Lower threshold ratio or check data range

Data Source

Local processing of NDVI/EVI time series data.

---

Advanced Usage

Batch Pixel Processing

bash

# Process a multi-band GeoTIFF stack (one pixel = one time series)
python scripts\phenology-metrics.py extract   --input ndvi_stack.csv --method logistic   --output phenology_results.csv

CI/CD Integration (GitHub Actions)

yaml

# .github/workflows/phenology-update.yml
name: Phenology Update
on:
  schedule:
    - cron: '0 0 1 3 *'  # Every March (growing season start)
jobs:
  extract:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install numpy scipy pandas
      - run: |
          python scripts\phenology-metrics.py extract \
            --input data/ndvi_2024.csv \
            --method logistic --output data/phenology_2024.csv

PostgreSQL Import

bash

python scripts\phenology-metrics.py extract   --input ndvi_stack.csv --method logistic --output phenology.csv

psql -d gis_db -c "\COPY phenology(pixel_id, sos, eos, los, peak_date, amplitude) FROM 'phenology.csv' CSV HEADER"

Performance Tips

• Use --method threshold for fastest results (least accurate)
• --method logistic is recommended for most vegetation types
• --sg-window 7 controls Savitzky-Golay smoothing window (larger = smoother)
• --max-gap 30 fills gaps up to 30 days; increase for cloudy regions

---

中文说明

从 NDVI/EVI 时间序列数据中提取植被物候指标。支持阈值法、导数法和双 Logistic 拟合。

功能特性

• 关键指标：SOS（生长季开始）、EOS（生长季结束）、LOS（生长季长度）、峰值、峰值日期、振幅、积分
• 阈值法：振幅的 10%/50% 阈值
• 导数法：拟合曲线的拐点
• 双 Logistic 拟合：平滑物候曲线
• 输入格式：CSV（日期, 值）或多波段 GeoTIFF
• 输出格式：CSV 和 JSON

物候指标

指标	说明
SOS	生长季开始（返青）
EOS	生长季结束（衰老）
LOS	生长季长度（EOS - SOS）
Peak	最大 NDVI/EVI 值
Peak Date	峰值日期
Amplitude	峰值 - 基线
Integral	积分

使用示例

从 CSV 提取

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_series.csv \
  --date-col date --value-col ndvi \
  --method threshold --threshold-ratio 0.5 \
  --output phenology.json

双 Logistic 拟合

bash

python scripts\phenology-metrics.py fit \
  --input ndvi_series.csv \
  --date-col date --value-col ndvi \
  --output fitted.json --plot-data plot.csv

生成绘图数据

bash

python scripts\phenology-metrics.py plot-data \
  --input ndvi_series.csv --date-col date --value-col ndvi \
  --output curve.csv

安装

bash

pip install numpy>=1.21.0 scipy>=1.7.0 pandas>=1.3.0
# 或: pip install -r scripts/requirements.txt

参数说明

• --input: 输入 CSV 文件（日期, 值列）
• --date-col: 日期列名（默认: date）
• --value-col: 值列名（默认: ndvi）
• --method: 提取方法（threshold, derivative, logistic）
• --threshold-ratio: SOS/EOS 阈值比率（默认: 0.5）
• --output: 输出文件路径
• --plot-data: 输出拟合曲线数据
• --json: 以 JSON 输出

输出结果

• 物候指标：CSV/JSON 格式的所有计算指标
• 拟合曲线：CSV 含日期、原始值、拟合值
• 统计信息：拟合优度（R²）

依赖库

text

numpy>=1.21.0
scipy>=1.7.0
pandas>=1.3.0

GeoTIFF 堆栈使用

使用多波段 GeoTIFF 堆栈（每个时间步一个波段）作为输入：

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_stack.tif \
  --input-format geotiff \
  --date-file dates.txt \
  --method logistic \
  --output phenology.json

• --input-format geotiff：指定 GeoTIFF 堆栈输入
• --date-file：每行一个日期的文本文件（YYYY-MM-DD），每个波段对应一个日期
• --band-epoch：波段编号参考日期（默认：波段 1 = 第一个日期）

日期格式说明

支持的日期格式：

格式	示例	说明
ISO 日期	2023-06-15	推荐（YYYY-MM-DD）
美国格式	06/15/2023	MM/DD/YYYY
DOY	166	年积日（1-366）
年-DOY	2023-166	组合格式

使用 --date-format 参数指定。

方法选择指南

方法	适用场景	优缺点
threshold	快速估算、稀疏数据	简单但不够精确
derivative	精确时间、平滑数据	对噪声敏感
logistic	平滑曲线、科研级	拟合最佳但需要干净数据

建议：探索性分析用 threshold；发表级结果用 logistic。

缺失数据处理

时间序列常有缺口（云、传感器问题）。工具通过以下方式处理：

• 插值：≤30 天缺口使用线性插值（可通过 --max-gap 配置）
• 缺口填充：Savitzky-Golay 平滑处理噪声数据
• 标记：在结果中输出数据质量标志

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_series.csv --method logistic \
  --max-gap 30 --sg-window 7 \
  --output phenology.json

多年数据处理

对于多年时间序列：

• 逐年提取：使用 --year-range 2020-2023 循环每年
• 指定年份：使用 --year 2022 进行单年分析
• 物候指标：每年单独计算，输出为多行 CSV/JSON

bash

python scripts\phenology-metrics.py extract \
  --input ndvi_series.csv --method logistic \
  --year-range 2020-2023 \
  --output phenology_multiyear.csv

多像元批量处理

处理 GeoTIFF 堆栈中的多个像元：

bash

python scripts\phenology-metrics.py batch \
  --input ndvi_stack.tif \
  --input-format geotiff \
  --method logistic \
  --output-dir ./phenology_results/ \
  --max-workers 4

输出每个像元（行, 列）一个 JSON 文件。

可视化

• 时间序列：绘制 NDVI/EVI 点 + 拟合曲线叠加
• 关键日期：SOS、EOS、峰值日期的垂直线
• 多年对比：分面图展示每年物候曲线

引用格式

bibtex

@software{phenology_metrics,
  author  = {ruiduobao},
  title   = {Phenology Metrics Extraction Tool},
  url     = {https://github.com/ruiduobao/phenology-metrics},
  version = {0.1.0},
  year    = {2024},
}

故障排除

错误	原因	解决方案
ConnectionError	网络问题	检查网络，重试
HTTP 429	速率限制	等待 60 秒后重试
ValueError	无效输入	检查参数格式
无输出	无有效数据	检查输入数据质量
ModuleNotFoundError	缺少依赖	运行 pip install
拟合差（低 R²）	数据噪声大	尝试 logistic 或增大 --sg-window
缺少 SOS/EOS	季节性弱	降低阈值比率或检查数据范围

数据来源

本地处理 NDVI/EVI 时间序列数据。